Swapped/marked remaining V1 methods in tests

libs/labelbox/tests/data/annotation_import/conftest.py

@@ -608,7 +608,7 @@ def get_data_row_id(indx=0):
 
     yield get_data_row_id
 
-
+#TODO: Switch to setup_editor, setup might get removed in later releases
 @pytest.fixture
 def configured_project(client, initial_dataset, ontology, rand_gen, image_url):
     dataset = initial_dataset
@@ -642,7 +642,7 @@ def configured_project(client, initial_dataset, ontology, rand_gen, image_url):
 
     project.delete()
 
-
+#TODO: Switch to setup_editor, setup might get removed in later releases
 @pytest.fixture
 def project_with_ontology(client, configured_project, ontology, rand_gen):
     project = client.create_project(name=rand_gen(str),
@@ -657,7 +657,7 @@ def project_with_ontology(client, configured_project, ontology, rand_gen):
 
     project.delete()
 
-
+#TODO: Switch to setup_editor, setup might get removed in later releases
 @pytest.fixture
 def configured_project_pdf(client, ontology, rand_gen, pdf_url):
     project = client.create_project(name=rand_gen(str),

libs/labelbox/tests/data/annotation_import/test_bulk_import_request.py

@@ -1,6 +1,6 @@
 from unittest.mock import patch
 import uuid
-from labelbox import parser
+from labelbox import parser, Project
 import pytest
 import random
 from labelbox.data.annotation_types.annotation import ObjectAnnotation
@@ -217,20 +217,20 @@ def test_delete(configured_project, predictions):
     assert len(list(all_import_requests)) == 0
 
 
-def test_pdf_mal_bbox(client, configured_project_pdf):
+def test_pdf_mal_bbox(client, configured_project_pdf:Project):
     """
     tests pdf mal against only a bbox annotation
     """
     annotations = []
     num_annotations = 1
 
-    for row in configured_project_pdf.export_queued_data_rows():
+    for data_row_id in configured_project_pdf.data_row_ids:
         for _ in range(num_annotations):
             annotations.append({
                 "uuid": str(uuid.uuid4()),
                 "name": "bbox",
                 "dataRow": {
-                    "id": row['id']
+                    "id": data_row_id
                 },
                 "bbox": {
                     "top": round(random.uniform(0, 300), 2),
@@ -247,14 +247,14 @@ def test_pdf_mal_bbox(client, configured_project_pdf):
                 'answer': 'the answer to the text question',
                 'uuid': 'fc1913c6-b735-4dea-bd25-c18152a4715f',
                 "dataRow": {
-                    "id": row['id']
+                    "id": data_row_id
                 }
             },
             {
                 'name': 'checklist',
                 'uuid': '9d7b2e57-d68f-4388-867a-af2a9b233719',
                 "dataRow": {
-                    "id": row['id']
+                    "id": data_row_id
                 },
                 'answer': [{
                     'name': 'option1'
@@ -269,14 +269,14 @@ def test_pdf_mal_bbox(client, configured_project_pdf):
                 },
                 'uuid': 'ad60897f-ea1a-47de-b923-459339764921',
                 "dataRow": {
-                    "id": row['id']
+                    "id": data_row_id
                 }
             },
             {  #adding this with the intention to ensure we allow page: 0 
                 "uuid": str(uuid.uuid4()),
                 "name": "bbox",
                 "dataRow": {
-                    "id": row['id']
+                    "id": data_row_id
                 },
                 "bbox": {
                     "top": round(random.uniform(0, 300), 2),

libs/labelbox/tests/data/annotation_import/test_data_types.py

@@ -145,6 +145,7 @@ def create_data_row_for_project(project, dataset, data_row_ndjson, batch_name):
 
 
 # TODO: Add VideoData. Currently label import job finishes without errors but project.export_labels() returns empty list.
+@pytest.mark.export_v1("tests used export v1 method, v2 test -> test_import_data_types_v2 below")
 @pytest.mark.parametrize(
     "data_type_class",
     [

libs/labelbox/tests/data/annotation_import/test_mea_prediction_import.py

@@ -1,6 +1,7 @@
 import uuid
 from labelbox import parser
 import pytest
+from labelbox import ModelRun
 
 from labelbox.schema.annotation_import import AnnotationImportState, MEAPredictionImport
 from labelbox.data.serialization import NDJsonConverter
@@ -107,29 +108,38 @@ def test_create_from_objects_all_project_labels(
         annotation_import.status_file_url)
 
 
-def test_model_run_project_labels(model_run_with_all_project_labels,
+def test_model_run_project_labels(model_run_with_all_project_labels: ModelRun,
                                   model_run_predictions):
     model_run = model_run_with_all_project_labels
-    # TODO: Move to export_v2
-    model_run_exported_labels = model_run.export_labels(download=True)
+
+    export_task = model_run.export()
+    export_task.wait_till_done()
+    stream = export_task.get_buffered_stream()
+
+    # exports to list of tuples (data_row_id, label) needed to adapt test to export v2 instead of export v1 since data rows ids are not at label level in export v2.
+    model_run_exported_labels = [(
+        data_row.json["data_row"]["id"],
+        data_row.json["experiments"][model_run.model_id]["runs"][model_run.uid]["labels"][0]) 
+        for data_row in stream]
+
     labels_indexed_by_schema_id = {}
 
-    for label in model_run_exported_labels:
+    for data_row_id, label in model_run_exported_labels:
         # assuming exported array of label 'objects' has only one label per data row... as usually is when there are no label revisions
-        schema_id = label['Label']['objects'][0]['schemaId']
-        labels_indexed_by_schema_id[schema_id] = label
+        schema_id = label["annotations"]["objects"][0]["feature_schema_id"]
+        labels_indexed_by_schema_id[schema_id] = {"label": label, "data_row_id": data_row_id}
 
     assert (len(
         labels_indexed_by_schema_id.keys())) == len(model_run_predictions)
 
     # making sure the labels are in this model run are all labels uploaded to the project
     # by comparing some 'immutable' attributes
     for expected_label in model_run_predictions:
-        schema_id = expected_label['schemaId']
+        schema_id = expected_label["schemaId"]
         actual_label = labels_indexed_by_schema_id[schema_id]
-        assert actual_label['Label']['objects'][0]['title'] == expected_label[
+        assert actual_label["label"]["annotations"]["objects"][0]["name"] == expected_label[
             'name']
-        assert actual_label['DataRow ID'] == expected_label['dataRow']['id']
+        assert actual_label["data_row_id"] == expected_label["dataRow"]["id"]
 
 
 def test_create_from_label_objects(model_run_with_data_rows,

libs/labelbox/tests/data/annotation_import/test_model_run.py

@@ -119,7 +119,7 @@ def test_model_run_upsert_data_rows_with_existing_labels(
     assert n_data_rows == len(
         list(model_run_with_data_rows.model_run_data_rows()))
 
-
+@pytest.mark.export_v1("tests used export v1 method, v2 test -> test_import_data_types_v2 below")
 def test_model_run_export_labels(model_run_with_data_rows):
     labels = model_run_with_data_rows.export_labels(download=True)
     assert len(labels) == 3

libs/labelbox/tests/data/annotation_import/test_send_to_annotate_mea.py

@@ -56,9 +56,14 @@ def test_send_to_annotate_from_model(client, configured_project,
     destination_batches = list(destination_project.batches())
     assert len(destination_batches) == 1
 
-    destination_data_rows = list(destination_batches[0].export_data_rows())
+    export_task = destination_project.export()
+    export_task.wait_till_done()
+    stream = export_task.get_buffered_stream()
+
+    destination_data_rows = [dr.json["data_row"]["id"] for dr in stream]
+
     assert len(destination_data_rows) == len(data_row_ids)
-    assert all([dr.uid in data_row_ids for dr in destination_data_rows])
+    assert all([dr in data_row_ids for dr in destination_data_rows])
 
     # Since data rows were added to a review queue, predictions should be imported into the project as labels
     destination_project_labels = (list(destination_project.labels()))

libs/labelbox/tests/data/export/streamable/test_export_project_streamable.py

@@ -313,7 +313,7 @@ def test_batch(
             },
         ] * 2)
         task.wait_till_done()
-        data_rows = [dr.uid for dr in list(dataset.export_data_rows())]
+        data_rows = [result["id"] for result in task.result]
         batch_one = f"batch one {uuid.uuid4()}"
 
         # This test creates two batches, only one batch should be exporter

libs/labelbox/tests/data/test_data_row_metadata.py

@@ -89,9 +89,13 @@ def test_export_empty_metadata(client, configured_project_with_label,
                                wait_for_data_row_processing):
     project, _, data_row, _ = configured_project_with_label
     data_row = wait_for_data_row_processing(client, data_row)
-    labels = project.label_generator()
-    label = next(labels)
-    assert label.data.metadata == []
+
+    export_task = project.export(params={"metadata_fields": True})
+    export_task.wait_till_done()
+    stream = export_task.get_buffered_stream()
+    data_row = [data_row.json for data_row in stream][0]
+
+    assert data_row["metadata_fields"] == []
 
 
 def test_bulk_export_datarow_metadata(data_row, mdo: DataRowMetadataOntology):